Entropy predicts early MEG, EEG and fMRI responses to natural images

To reduce the redundancy in the input, the human visual system employs efficient coding. Therefore, images with varying entropy (amount of information) should elicit distinct brain responses. Here, we show that a simple entropy model outperforms all current models, including many deep neural networks, in predicting early MEG/EEG and fMRI responses to visual objects. This suggests that the neural populations in the early visual cortex adapt to the information in natural images.